This is neat! I’m not fully through it yet, but just wanted to emphasize this:
> And understanding molecular motion is key for everything in biology, everything in biology is vibrating molecules underneath the surface!
Coming into bio as a programmer, this is the absolute sin qua non rule you need to internalize: there are no boundaries between systems, because everything is jiggling atoms. DNA encodes for genes, except the transcription process is heavily mediated by the physical environment and physical constraints of accessing the DNA; RNA transcribes to amino acid strings, except it’s also a molecule, and so sometimes it folds into a structure and just does shit itself; proteins have a function, except sometimes they have many functions, because the “lock and key” metaphor isn’t wrong, except when you’ve got a billion locks and your key’s kinda floppy, it’ll probably fit more than one. Nature plays with physical systems and will repurpose anything to do anything else - the informatics only take you so far, all the real action is vibrating molecules.
holodro 14 hours ago [-]
> Coming into bio as a programmer, this is the absolute sin qua non rule you need to internalize: there are no boundaries between systems, because everything is jiggling atoms.
(Similar background as you.) Another sine qua non rule is that evolution created biology, it wasn't engineered like software and it doesn't decompose like software. Evolution creates hairballs that has don't respect traditional engineering boundaries and abstraction hierarchies.
From that, along with probabilistic molecular jiggling, we get biological systems that are quite difficult to understand, predict, and control.
kurthr 13 hours ago [-]
It's a good start to realize that what underlies all the understanding of science are simplified predictive models, and usually only statistical models at that.
What this means is that running an experiment in many fields is so difficult that replication is a real challenge. There are so MANY ways you can screw up, or you could just have a statistical fluke that screws you over. Just a tiny contamination or seemingly irrelevant missed step will cause a failure. That's why the idea of having journals composed of failed experiments just doesn't work. Unstated experimental process assumptions are legion. Sometimes an expert can look at the result and see what you've done wrong (like bad contacts in "Electron Band Structure In Germanium, My Ass") and often not even that. Sometimes there's something interesting in the failure, but 99% of the time it's just your pitch is so bad you can't hit the strike zone. Do better!
The things that are easy to replicate (and usually they've been specifically designed that way like Starbucks' over roasted beans), have actually been reduced to engineering. They're not on the edge where scientists can get published. That way perverse incentive madness lies.
Enjoy the controlability of inputs, the repeatability of bugs, the near perfection of compilers and memory allocation, the complete independence of variables while you can. Unless that is, you like Rowhammer and voltage glitch attacks.
seamossfet 17 hours ago [-]
Great write up, we're working on a drug discovery CAD tool and MD has been one of our focal points. Extremely challenging and fun problem to work on!
What complicates things is the experimental data we get back from labs to validate MD behavior is extremely tricky to work with. Most of what we're working with is NMR data which shows flexibility in areas of the proteins, but even then we're left with these mathematical models to attempt to "make sense" of the flexibility and infer dynamics from that. Sometimes it feels like an art and a science trying to get meaningful insights for lab data like this.
It's extremely difficult to experimentally verify any MD model since, as mentioned in the article, most of the data we're working with are static mugshots in the form of crystal structures.
forgotpwagain 14 hours ago [-]
Very cool. There are also methods that allow you to extract some notion of motion from variability in CryoEM data, e.g. CryoDRGN-ET [1].
I'm curious if you've worked with any of those models and how they relate to NMR data and MD simulations.
There are also techniques that combine both. In my experience (as an experimental structural biologist working in drug design), they frequently disagree.
the__alchemist 16 hours ago [-]
That's so cool! What's the software like, compared to say, PyMol? Is it like PyMol, integrated with docking? Are you using MD to position the drugs instead of trying different combos, like Vina does?
edwardbernays 12 hours ago [-]
hello, I have an undergrad degree in computer science and I'm trying to reach myself informatics to get into this field. do you have any tips, or perhaps an internship available?
if you can reach out at all, you can find me at [masterfully dot blundered] on the normal g-domain. I briefly skimmed your profile for contact info but could not find any.
max_ 15 hours ago [-]
There is brilliant video by the hedge fund manager DE Shaw about molecular dynamics simulation.
MD is a great entry point for anyone interested in scientific computing. A naive simulation is super easy to implement but you quickly learn hard lessons regarding performance scaling. I wrote an MD engine as a demo project for learning the basics of CUDA C.
For anyone with further interest in MD, two of the popular engines, Amber and Gromacs have excellent documentation for learning (1, 2). MDAnalysis is a popular analysis package. Their docs give a great rundown of what type of information you can glean from MD (3). If you’re strictly interested in eye candy, there’s a a fabulous blender plugin for visualizing MD simulations and protein structures (4). I also wrote a little Python program for setting up simulation systems you can do some fun stuff with it (5).
Amazing article on Molecular dynamics, in the infinite number of things they could add is a small segment on coarse graining. Though I'm biased (and have been thinking about writing one myself).
Granted wished this had been around when I started my journey instead of having to delve into things like the Amber manual... (which I will grant is wonderful for its information but the organization isn't as convenient).
abhishaike 16 hours ago [-]
Author here, I wish I added a section on coarse graining as well :) hope you write a post about it!
fentonc 16 hours ago [-]
Fun article! I was one of the architects on Anton 2 and Anton 3 at DESRES.
max_ 14 hours ago [-]
Hi,
Do you have any resources that you recommend on coarse graining?
I am really interested in the topic.
frgoe 13 hours ago [-]
I am currently working on CG potentials. Can really recommend the basics from Gregory A. Voth.
viapivov 15 hours ago [-]
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varelse 18 hours ago [-]
[dead]
Rendered at 08:53:45 GMT+0000 (Coordinated Universal Time) with Vercel.
> And understanding molecular motion is key for everything in biology, everything in biology is vibrating molecules underneath the surface!
Coming into bio as a programmer, this is the absolute sin qua non rule you need to internalize: there are no boundaries between systems, because everything is jiggling atoms. DNA encodes for genes, except the transcription process is heavily mediated by the physical environment and physical constraints of accessing the DNA; RNA transcribes to amino acid strings, except it’s also a molecule, and so sometimes it folds into a structure and just does shit itself; proteins have a function, except sometimes they have many functions, because the “lock and key” metaphor isn’t wrong, except when you’ve got a billion locks and your key’s kinda floppy, it’ll probably fit more than one. Nature plays with physical systems and will repurpose anything to do anything else - the informatics only take you so far, all the real action is vibrating molecules.
(Similar background as you.) Another sine qua non rule is that evolution created biology, it wasn't engineered like software and it doesn't decompose like software. Evolution creates hairballs that has don't respect traditional engineering boundaries and abstraction hierarchies.
From that, along with probabilistic molecular jiggling, we get biological systems that are quite difficult to understand, predict, and control.
What this means is that running an experiment in many fields is so difficult that replication is a real challenge. There are so MANY ways you can screw up, or you could just have a statistical fluke that screws you over. Just a tiny contamination or seemingly irrelevant missed step will cause a failure. That's why the idea of having journals composed of failed experiments just doesn't work. Unstated experimental process assumptions are legion. Sometimes an expert can look at the result and see what you've done wrong (like bad contacts in "Electron Band Structure In Germanium, My Ass") and often not even that. Sometimes there's something interesting in the failure, but 99% of the time it's just your pitch is so bad you can't hit the strike zone. Do better!
The things that are easy to replicate (and usually they've been specifically designed that way like Starbucks' over roasted beans), have actually been reduced to engineering. They're not on the edge where scientists can get published. That way perverse incentive madness lies.
Enjoy the controlability of inputs, the repeatability of bugs, the near perfection of compilers and memory allocation, the complete independence of variables while you can. Unless that is, you like Rowhammer and voltage glitch attacks.
What complicates things is the experimental data we get back from labs to validate MD behavior is extremely tricky to work with. Most of what we're working with is NMR data which shows flexibility in areas of the proteins, but even then we're left with these mathematical models to attempt to "make sense" of the flexibility and infer dynamics from that. Sometimes it feels like an art and a science trying to get meaningful insights for lab data like this.
It's extremely difficult to experimentally verify any MD model since, as mentioned in the article, most of the data we're working with are static mugshots in the form of crystal structures.
I'm curious if you've worked with any of those models and how they relate to NMR data and MD simulations.
[1] https://www.nature.com/articles/s41592-024-02340-4
I've also written a potentially helpful coverage piece on extracting conformations from cryo-EM data: https://www.owlposting.com/p/a-primer-on-ml-in-cryo-electron...
if you can reach out at all, you can find me at [masterfully dot blundered] on the normal g-domain. I briefly skimmed your profile for contact info but could not find any.
Its very accessible and I found it very interesting — https://youtu.be/PGqCeSjNuTY?feature=shared
For anyone with further interest in MD, two of the popular engines, Amber and Gromacs have excellent documentation for learning (1, 2). MDAnalysis is a popular analysis package. Their docs give a great rundown of what type of information you can glean from MD (3). If you’re strictly interested in eye candy, there’s a a fabulous blender plugin for visualizing MD simulations and protein structures (4). I also wrote a little Python program for setting up simulation systems you can do some fun stuff with it (5).
(1) https://ambermd.org/Manuals.php
(2) https://manual.gromacs.org/current/index.html
(3) https://www.mdanalysis.org/pages/documentation/
(4) https://bradyajohnston.github.io/MolecularNodes/
(5) https://github.com/AppleIntusion/MMAEVe
Granted wished this had been around when I started my journey instead of having to delve into things like the Amber manual... (which I will grant is wonderful for its information but the organization isn't as convenient).
Do you have any resources that you recommend on coarse graining?
I am really interested in the topic.